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相关概念视频

PD Controller: Design01:26

PD Controller: Design

184
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
184
PI Controller: Design01:24

PI Controller: Design

200
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
200
Controller Configurations01:22

Controller Configurations

85
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
85
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

83
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
83
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

2.5K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
2.5K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

464
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
464

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基于MPC的自动驾驶汽车路径跟踪控制器的数据驱动增强

Jianhua Guo1, Zhihao Xie1, Ming Liu2

  • 1National Key Laboratory of Automotive Chassis Integration and Bionics, Changchun 130022, China.

Sensors (Basel, Switzerland)
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概括
此摘要是机器生成的。

这项研究通过使用数据驱动方法来增强自动驾驶汽车路径跟踪. 新方法提高了准确性,在具有挑战性的条件下超过标准控制器.

关键词:
数据驱动的数据驱动.模型预测控制模型预测控制路径跟踪跟踪路径跟踪

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科学领域:

  • 自主系统 自主系统
  • 控制理论 控制理论
  • 机器学习 机器学习

背景情况:

  • 准确的控制模型对于自动驾驶汽车的基于模型的控制至关重要.
  • 由于简化,参数变化和噪音降低路径跟踪精度而导致的模型不准确性.

研究的目的:

  • 为自动驾驶汽车的基于模型预测控制 (MPC) 的路径跟踪控制器 (DD-PTC) 引入数据驱动的增强功能.
  • 通过提高控制模型准确度来提高车辆路径跟踪的精度.

主要方法:

  • 使用Kolmogorov-Arnold网络 (KANs) 来估计轮胎侧向力和纠正动态预测模型的拐角刚度.
  • 采用高斯过程回归 (GPR) 来捕捉未建模的车辆动态,用于全面的控制模型.
  • 在方向盘控制的MPC框架内实施增强型号.

主要成果:

  • 与标准的MPC和线性二次调节器 (LQR) 策略相比,DD-PTC方法显示出更高的性能.
  • 观察到横向距离错误的显著减少,特别是在具有挑战性的驾驶场景下.
  • 验证使用Simulink-CarSim模拟平台进行.

结论:

  • 拟议的数据驱动增强有效地提高了自动驾驶汽车路径跟踪控制模型的准确性.
  • DD-PTC为精确的方向盘控制提供了强大的解决方案,性能优于传统方法.
  • KAN和GPR的整合提供了一个强大的框架,用于解决自动驾驶中的模型不确定性.